New York City Taxi Trip Duration

평가함수(Evalution)

  • RMSLE(Root Mean Squared Logarithmic Error) \[\epsilon = \sqrt{\frac{1}{n} \sum_{i=1}^n (\log(p_i + 1) - \log(a_i+1))^2 }\]
id,trip_duration
id00001,978
id00002,978
id00003,978
id00004,978
etc.

Data 소개

  • 경쟁 데이터 세트는 Google Cloud Platform의 Big Query에서 제공되는 2016 년 NYC Yellow Cab 여행 기록 데이터를 기반으로합니다.

  • 이 데이터는 원래 NYC 택시 및 리무진위원회 (TLC)에서 발간 한 것입니다.

  • 데이터는 이 놀이터 경쟁의 목적을 위해 샘플링되고 청소되었습니다.
  • 참가자는 개별 여행 속성에 따라 테스트 세트의 각 여행 기간을 예측해야 합니다.

데이터 필드

  • id : 각 출장의 고유 식별자
  • vendor_id : 여행 기록과 연결된 공급자를 나타내는 코드
  • pickup_datetime : 미터가 작동 된 날짜와 시간
  • dropoff_datetime : 미터가 분리 된 날짜와 시간
  • passenger_count : 차량의 승객 수 (운전자가 입력 한 값)
  • pickup_longitude : 미터가 사용 된 경도
  • pickup_latitude : 미터가 사용 된 위도
  • dropoff_longitude : 미터가 분리 된 경도
  • dropoff_latitude : 미터가 분리 된 위도
  • store_and_fwd_flag : 플래그는 자동차가 서버에 연결되어 있지 않아 여행 기록이 차량 메모리에 보관되었는지 여부를 나타냅니다.
  • Y = 저장 및 전달; N = 상점 및 순회 여행 불가

  • trip_duration : 여행 기간 (초)

  • 면책 조항 : 커널에서 사용할 확장 된 변수 집합을 제공하기 위해 데이터 집합 순서에서 드롭 오프 좌표를 제거하지 않기로 결정했습니다.

1단계. 데이터 전처리

#install.packages(c('flexdashboard', 'TraMineR', 'leaflet', 'treemap', 'highcharter', 'zoo')
#라이브러리 로딩
library(data.table)
library(dplyr)
library(ggplot2)
library(flexdashboard)
library(TraMineR)
library(highcharter)
library(DT)
library(flexdashboard)
library(leaflet)
library(rmarkdown)
library(treemap)
library(viridisLite)
library(tidyverse)
library(geosphere)
library(caret)
library(ggmap)
library(scales)
library(ggthemes)
library(gridExtra)
library(sp)
library(lubridate)
library(grid)
rm(list=ls())
fillColor = "#ff9999"
train = read_csv("./data/train.csv")
Parsed with column specification:
cols(
  id = col_character(),
  vendor_id = col_integer(),
  pickup_datetime = col_datetime(format = ""),
  dropoff_datetime = col_datetime(format = ""),
  passenger_count = col_integer(),
  pickup_longitude = col_double(),
  pickup_latitude = col_double(),
  dropoff_longitude = col_double(),
  dropoff_latitude = col_double(),
  store_and_fwd_flag = col_character(),
  trip_duration = col_integer()
)

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test = read_csv("./data/test.csv")
Parsed with column specification:
cols(
  id = col_character(),
  vendor_id = col_integer(),
  pickup_datetime = col_datetime(format = ""),
  passenger_count = col_integer(),
  pickup_longitude = col_double(),
  pickup_latitude = col_double(),
  dropoff_longitude = col_double(),
  dropoff_latitude = col_double(),
  store_and_fwd_flag = col_character()
)
sum(is.na(train))
[1] 0
sum(is.na(test))
[1] 0
ggplot(data=train, aes(x= trip_duration)) + 
  geom_histogram(bins = 100) +
  scale_x_log10(limits = c(NA,100000)) +
  scale_y_log10() +
  theme_bw() +
  theme(axis.title = element_text(size=16),
        axis.text = element_text(size=14)) +
  labs(x = 'Trip Duration', y = 'Count', title = 'Trip Duration') 

nycData = subset(train,train$trip_duration < (60*60*24) )
ggplot(nycData,aes(x=factor(passenger_count),y=trip_duration))+geom_boxplot()+scale_y_log10()

ggplot(data=nycData, aes(x= pickup_longitude)) + 
  geom_histogram(bins = 100) +
  scale_x_continuous(limits = c(-74,-73.85)) +
  theme_bw() +
  theme(axis.title = element_text(size=16),
        axis.text = element_text(size=14)) +
  labs(x = 'Longitude', y = 'Count', title = 'Longitude')
Warning message:
In strsplit(content, "\n", fixed = TRUE) : input string 1 is invalid UTF-8

ggplot(data=nycData, aes(x= pickup_latitude)) + 
  geom_histogram(bins = 100) +
  scale_x_continuous(limits = c(40.6,40.85)) +
  theme_bw() +
  theme(axis.title = element_text(size=16),
        axis.text = element_text(size=14)) +
  labs(x = 'Latitude', y = 'Count', title = 'Latitude')

pick_coord <- nycData %>%
  select(pickup_longitude, pickup_latitude)
drop_coord <- nycData %>%
  select(dropoff_longitude, dropoff_latitude)
nycData$dist <- distCosine(pick_coord, drop_coord) 
nycData$haversine <- distHaversine(pick_coord, drop_coord)
nycData$bearing <- bearing(pick_coord, drop_coord) 
ggplot(data=nycData, aes(x= haversine)) + 
  geom_histogram() +
  scale_x_log10() +
  scale_y_log10() +
  theme_bw() +
  theme(axis.title = element_text(size=16),
        axis.text = element_text(size=14)) +
  labs(x = 'Distance', y = 'Count', title = 'Distance')

ggplot(nycData)+
  geom_point(aes(x=haversine,y=trip_duration))+
  scale_y_log10() +
  scale_x_log10() +
  theme_bw()+
  theme(axis.title = element_text(size=16),axis.text = element_text(size=14))+
  xlab("(Distance)")+
  ylab("Duration")

COMBINING TRAIN AND TEST

train <- as.tibble(train)
test <- as.tibble(test)
combine = bind_rows(train %>% mutate(dset="train"),
                    test %>% mutate(dset="test",
                                    dropoff_datetime=NA,
                                    trip_duration=NA))
combine <- combine %>% mutate(dset = factor(dset))
train = train %>%
  mutate(pickup_datetime = ymd_hms(pickup_datetime),
         dropoff_datetime = ymd_hms(dropoff_datetime),
         vendor_id = factor(vendor_id),
         passenger_count = factor(passenger_count))

INDIVISUAL FEATURE VISUALIZATION

각 변수의 분포를 우선 살펴본다

pickup/dropoff coordinates

set.seed(1234)
foo <- sample_n(train, 8e3)
leaflet(data = foo) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
  addCircleMarkers(~ pickup_longitude, ~pickup_latitude, radius = 1,
                   color = "blue", fillOpacity = 0.3)

leaflet(data = foo) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
  addCircleMarkers(~ dropoff_longitude, ~dropoff_latitude, radius = 1,
                   color = "blue", fillOpacity = 0.3)

trip_duration

train %>%
  ggplot(aes(trip_duration)) +
  geom_histogram(fill = "red", bins = 150) + 
  scale_x_log10() +
  scale_y_sqrt()

# View(train)
p1 <- train %>%
  ggplot(aes(pickup_datetime)) +
  geom_histogram(fill = "red", bins = 120) +
  labs(x = "Pickup dates")
p2 <- train %>%
  ggplot(aes(dropoff_datetime)) +
  geom_histogram(fill = "blue", bins = 120) +
  labs(x = "Dropoff dates")
layout <- matrix(c(1,2),2,1,byrow=FALSE)
multiplot(p1, p2, layout=layout)

p1 <- train %>%
  group_by(passenger_count) %>%
  count() %>%
  ggplot(aes(passenger_count, n, fill = passenger_count)) +
  geom_col() +
  scale_y_sqrt() +
  theme(legend.position = "none")
p2 <- train %>%
  ggplot(aes(vendor_id, fill = vendor_id)) +
  geom_bar() +
  theme(legend.position = "none")
p3 <- train %>%
  ggplot(aes(store_and_fwd_flag)) +
  geom_bar() +
  theme(legend.position = "none") +
  scale_y_log10()
p4 <- train %>%
  mutate(wday = wday(pickup_datetime, label = TRUE)) %>%
  group_by(wday, vendor_id) %>%
  count() %>%
  ggplot(aes(wday, n, colour = vendor_id)) +
  geom_point(size = 4) +
  labs(x = "Day of the week", y = "Total number of pickups") +
  theme(legend.position = "none")
p5 <- train %>%
  mutate(hpick = hour(pickup_datetime)) %>%
  group_by(hpick, vendor_id) %>%
  count() %>%
  ggplot(aes(hpick, n, color = vendor_id)) +
  geom_point(size = 4) +
  labs(x = "Hour of the day", y = "Total number of pickups") +
  theme(legend.position = "none")
layout <- matrix(c(1,2,3,4,5,5),3,2,byrow=TRUE)
multiplot(p1, p2, p3, p4, p5, layout=layout)

p1 <- train %>%
  mutate(hpick = hour(pickup_datetime),
         Month = factor(month(pickup_datetime, label = TRUE))) %>%
  group_by(hpick, Month) %>%
  count() %>%
  ggplot(aes(hpick, n, color = Month)) +
  geom_line(size = 1.5) +
  labs(x = "Hour of the day", y = "count")
p2 <- train %>%
  mutate(hpick = hour(pickup_datetime),
         wday = factor(wday(pickup_datetime, label = TRUE))) %>%
  group_by(hpick, wday) %>%
  count() %>%
  ggplot(aes(hpick, n, color = wday)) +
  geom_line(size = 1.5) +
  labs(x = "Hour of the day", y = "count")
layout <- matrix(c(1,2),2,1,byrow=FALSE)
multiplot(p1, p2, layout=layout)

FEATURE RELATIONS

Pickup date/time vs trip_duration

p1 <- train %>%
  mutate(wday = wday(pickup_datetime, label = TRUE)) %>%
  group_by(wday, vendor_id) %>%
  summarise(median_duration = median(trip_duration)/60) %>%
  ggplot(aes(wday, median_duration, color = vendor_id)) +
  geom_point(size = 4) +
  labs(x = "Day of the week", y = "Median trip duration [min]")
p2 <- train %>%
  mutate(hpick = hour(pickup_datetime)) %>%
  group_by(hpick, vendor_id) %>%
  summarise(median_duration = median(trip_duration)/60) %>%
  ggplot(aes(hpick, median_duration, color = vendor_id)) +
  geom_smooth(method = "loess", span = 1/2) +
  geom_point(size = 4) +
  labs(x = "Hour of the day", y = "Median trip duration [min]") +
  theme(legend.position = "none")
layout <- matrix(c(1,2),2,1,byrow=FALSE)
multiplot(p1, p2, layout=layout)

Passenger count and Vendor vs trip_duration

train %>%
  ggplot(aes(trip_duration, fill = vendor_id)) +
  geom_density(position = "stack") +
  scale_x_log10()

train %>%
  group_by(vendor_id) %>%
  summarise(mean_duration = mean(trip_duration),
            median_duration = median(trip_duration))

Feature Engineering

train = as.data.table(train)
train <- train[,distance_km := 
                     distHaversine(matrix(c(pickup_longitude, pickup_latitude), ncol = 2),
                     matrix(c(dropoff_longitude,dropoff_latitude), ncol = 2))/1000
              ]
train %>% 
  ggplot(aes(x=distance_km)) + 
  geom_histogram(bins=4000, fill="red")+
  theme_bw()+theme(axis.title = element_text(size=11),axis.text = element_text(size=8))+
  ylab("Density")+coord_cartesian(x=c(0,25))

train[,speed:=(distance_km)/(trip_duration/3600)]
train %>% 
  ggplot(aes(x=speed)) + 
  geom_histogram(bins=4000, fill="red")+
  theme_bw()+theme(axis.title = element_text(size=11),axis.text = element_text(size=8))+
  ylab("Density")+coord_cartesian(x=c(0,50))

summary(train$speed)
    Min.  1st Qu.   Median 
   0.000    9.131   12.810 
    Mean  3rd Qu.     Max. 
  14.440   17.860 9285.000 
train$pickup_hour <- hour(train$pickup_datetime)
train$pickup_week <- week(train$pickup_datetime)
train$pickup_month <- month(train$pickup_datetime)
train$pickup_weekdays <- weekdays(train$pickup_datetime)
train$pickup_weekend <- ifelse(train$pickup_weekdays==1 | train$pickup_weekdays==7,"Weekend","not-Weekend")
train[,pickup_datetime:=as.Date(pickup_datetime)]
train[,dropoff_datetime:=as.Date(dropoff_datetime)]
train[,":="(
   pickup_yday=yday(pickup_datetime)
  ,pickup_mday=mday(pickup_datetime)
)]
train %>% 
  group_by(pickup_hour) %>% 
  summarize(mean_speed = mean(speed),n()) %>% 
  ggplot(aes(x=pickup_hour,y=mean_speed))+
  geom_smooth(method = 'loess',color="grey10")+
  geom_point(color="red")+coord_cartesian(ylim=c(10,25))+theme_bw()

Feature Visualisation

library(corrplot)
corr_features = train[,.(pickup_hour, pickup_week, pickup_month,pickup_yday, pickup_mday,passenger_count,trip_duration,distance_km)]
corrplot(cor(corr_features, use='complete.obs'), type='lower')

plot1 <-train[, list(mean_trip_duration = mean(trip_duration)), by = pickup_weekdays] %>%
  ggplot(aes(x = pickup_weekdays, y = mean_trip_duration)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Month', y = 'Mean Trip Duration', title = 'Mean Trip duration by weekdays')
grid.arrange(plot1)

plot1 <-train[, list(mean_trip_duration = mean(trip_duration)), by = pickup_hour] %>%
  ggplot(aes(x = as.factor(pickup_hour), y = mean_trip_duration)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Hours', y = 'Mean Trip Duration', title = 'Mean Trip duration by hour of the day')
plot2 = train[,.N, by=pickup_hour] %>%
  ggplot(aes(x=pickup_hour, y=N)) + 
  geom_bar(stat='identity', fill='steelblue') + 
  labs(x='', y='Number of Rides', title='Total Rides Per Hour')
grid.arrange(plot1, plot2, ncol =2)

External Data

suppressMessages({
fastest_route_train = read_csv("./data/new-york-city-taxi-with-osrm/fastest_routes_train_part_1.csv")
})

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dtrain = merge(train, fastest_route_train, by="id")
dtrain[,number_of_streets := number_of_steps - 1]
plot1 <- 
  dtrain[, list(mean_trip_duration = mean(total_travel_time)), by = number_of_streets] %>%
  ggplot(aes(x = as.factor(number_of_streets), y = mean_trip_duration)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Number of Streets', y = 'Mean Trip Duration', title = 'Mean Trip duration by Number of Streets')
plot2 <- dtrain[, list(Number_of_Rides = .N), by = number_of_streets] %>%
  ggplot(aes(x = as.factor(number_of_streets), y = Number_of_Rides)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Number of Streets', y = 'Number of Trips', title = 'Number of Rides by Number of Streets')
plot3 <- dtrain[, list(mean_distance = mean(total_distance)/1000), by = number_of_streets] %>%
  ggplot(aes(x = as.factor(number_of_streets), y = mean_distance)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Number of Streets', y = 'Mean Trip Distnace(km)', title = 'Mean Trip Distance by Number of Streets')
grid.arrange(plot1,plot2, plot3)

plot1 <- dtrain %>%
  ggplot(aes(trip_duration)) +
  geom_density(fill = "red", alpha = 0.5) +
  geom_density(aes(total_travel_time), fill = "blue", alpha = 0.5) +
  scale_x_log10() +
  coord_cartesian(xlim = c(5e1, 8e3))
dtrain[,diff:= abs(trip_duration-total_travel_time)]
dtrain[,number_of_steps:= ifelse(number_of_steps>= 25, 25, number_of_steps)]
plot2 =  dtrain[, list(mean_distance_km = mean(diff)), by=number_of_steps-1] %>%
  ggplot(aes(x=number_of_steps, y=mean_distance_km)) + 
  geom_bar(stat='identity', fill='steelblue') + 
  labs(x='Number of Streets', y='|TripDuration - TotalTravelTime)|')
grid.arrange(plot1, plot2, ncol=2)

Google Distance Matrix

suppressMessages({
google_dist = read_csv("./data/new-york-city-taxi-with-osrm/train_distance_matrix.csv")
})
google_dist = data.table(google_dist)
google_dist[, diff := google_duration-trip_duration]
plot1 <- google_dist %>%
  ggplot(aes(trip_duration)) +
  geom_density(fill = "red", alpha = 0.5) +
  geom_density(aes(google_duration), fill = "blue", alpha = 0.5) +
  scale_x_log10() +
  coord_cartesian(xlim = c(5e1, 8e3))
plot2 = google_dist %>% 
  ggplot(aes(x=diff)) + 
  geom_histogram(bins=20000, fill="red")+
  theme_bw()+theme(axis.title = element_text(size=12),axis.text = element_text(size=12))+
  ylab("Density")+coord_cartesian(x=c(-2000,2000))
grid.arrange(plot1, plot2, ncol=2)

Weather Data

weather = fread("./data/new-york-city-taxi-with-osrm/weather_data_nyc_centralpark_2016.csv")
weather <- weather %>%
  mutate(date = dmy(date),
         rain = as.numeric(ifelse(precipitation == "T", "0.01", precipitation)),
         s_fall = as.numeric(ifelse(`snow fall` == "T", "0.01", `snow fall`)),
         s_depth = as.numeric(ifelse(`snow depth` == "T", "0.01", `snow depth`)),
         all_precip = s_fall + rain,
         has_snow = (s_fall > 0) | (s_depth > 0),
         has_rain = rain > 0,
         max_temp = `maximum temerature`,
         min_temp = `minimum temperature`)
weather = as.data.table(weather)
weather[, c("precipitation", "snow fall", "snow depth", "maximum temerature", "minimum temperature") := NULL]
setkey(dtrain, dropoff_datetime)
setkey(weather, date)
dtrain = weather[dtrain]
plot1 = dtrain %>%
  group_by(pickup_hour, has_snow) %>%
  summarise(duration = mean(trip_duration)) %>%
  ggplot(aes(pickup_hour,duration, color = has_snow)) +
  geom_jitter(width = 0.01, size = 2) +
  labs(x = "hour", y = "trip duration")
plot2 = dtrain %>% 
  group_by(pickup_hour, has_snow) %>%
  summarise(distance = mean(distance_km)) %>%
  ggplot(aes(pickup_hour,distance, color = has_snow)) +
  geom_jitter(width = 0.01, size = 2) +
  labs(x = "hour", y = "Distance Covered")
grid.arrange(plot1, plot2,ncol=1)

plot1 = dtrain %>%
  group_by(pickup_hour, has_rain) %>%
  summarise(duration = mean(trip_duration)) %>%
  ggplot(aes(pickup_hour,duration, color = has_rain)) +
  geom_jitter(width = 0.01, size = 2) +
  labs(x = "hour", y = "trip duration")
plot2 = dtrain %>% 
  group_by(pickup_hour, has_rain) %>%
  summarise(distance = mean(distance_km)) %>%
  ggplot(aes(pickup_hour,distance, color = has_rain)) +
  geom_jitter(width = 0.01, size = 2) +
  labs(x = "hour", y = "Distance Covered")
grid.arrange(plot1, plot2,ncol=1)

METAR

train <-  fread("./data/train.csv")

Read 30.2% of 1458644 rows
Read 48.0% of 1458644 rows
Read 65.8% of 1458644 rows
Read 83.6% of 1458644 rows
Read 1458644 rows and 11 (of 11) columns from 0.187 GB file in 00:00:06
weather_nyc <-  fread("./data/KNYC_Metars.csv")
train[, pi_dt_shift :=  paste(substr(pickup_datetime, 1, 13), ":00:00", sep = "")]
train[, df_dt_shift := paste(substr(dropoff_datetime, 1, 13), ":00:00", sep = "")]
train_joined <- dplyr::left_join(train, weather_nyc, by  = c("pi_dt_shift" = "Time"))
train_joined$Conditions[is.na(train_joined$Conditions) ==  TRUE] <- "Unknown"
weather_condition_freq <- train_joined %>%
  group_by(Conditions) %>%
  select(Conditions,trip_duration ) %>%
  summarize(count = n(), 
            mean_dur = mean(trip_duration, na.rm = TRUE), 
            sd_dur =   sd(trip_duration, na.rm = TRUE), 
            median_dur = median(trip_duration, na.rm = TRUE))
datatable(weather_condition_freq)
highchart()%>%
  hc_add_series(weather_condition_freq, "spline", hcaes(x =  Conditions, y = mean_dur), name = "Mean Trip Duration") %>%
  hc_add_series(weather_condition_freq, "spline", hcaes(x =  Conditions, y = median_dur), name = "Median Trip Duration") %>%
  hc_add_series(weather_condition_freq, "spline", hcaes(x =  Conditions, y = sd_dur), name = "SD Trip Duration") %>%
  hc_plotOptions(series = list(
    showInLegend = TRUE, 
    pointFormat = "{point.y}%"
  ), 
  column = list(colorByPoint = TRUE)) %>%
  hc_subtitle(text = "Count by Conditions Caegories") %>%
  hc_credits(
    enabled = TRUE, 
    text = "Source: Kaggle", 
    href = "https://kaggle.com/damianpanek", 
    style = list(fontSize = "12px")
  ) %>%
  hc_add_theme(hc_theme_google())
train_joined <- data.table(train_joined)
train_joined <- train_joined[is.na(pickup_datetime) == FALSE,  ]
train_joined[, pickup_datetime := as.POSIXct(pickup_datetime, format = "%Y-%m-%d %H:%M:%S")]
train_joined[, dropoff_datetime := as.POSIXct(dropoff_datetime, format = "%Y-%m-%d %H:%M:%S")]
train_joined[, pickup_day := format(pickup_datetime, "%Y-%m-%d")]
train_joined[, pickup_month := format(pickup_datetime, "%Y-%m")]
train_joined[, dropoff_day := format(dropoff_datetime, "%Y-%m-%d")]
train_joined[, dropoff_month := format(dropoff_datetime, "%Y-%m")]
train_joined[, weekday := weekdays(pickup_datetime)]
weather_temp_day <-  train_joined %>% 
  group_by(pickup_day) %>%
  select(pickup_day, Temp., Conditions) %>%
  summarize(count = n(), 
            min = min(Temp., na.rm = TRUE), 
            max = max(Temp., na.rm = TRUE), 
            sd_dur = sd(Temp., na.rm = TRUE))

hchart(weather_temp_day, 
        type = "columnrange", 
        hcaes(x = pickup_day, low = min, high = max, color = sd_dur)) %>%
        hc_chart(polar = TRUE) %>%
    hc_yAxis(max = 30,  min = -10, labels = list(format = "{value} "), 
             showFirstLabel = FALSE) %>%
  hc_xAxis(
  title = list(text = ""), gridLineWidth = 0.5,
  labels = list(format = "{value: %b}")) %>%
  hc_add_theme(hc_theme_google()) %>%
hc_title(text = "Min/Max temperature daily, coloured by SD(Temp)")

Leaflet section

#install.packages('leaflet.extras')
library(leaflet)
library(leaflet.extras)
lon_lat <- train_joined[, c("pickup_longitude", "pickup_latitude", 
"dropoff_longitude", "dropoff_latitude")]
lon_lat$rown <- as.numeric(rownames(lon_lat))
lon_min <- lon_lat[rown < 300 ,]
str(lon_min)
Classes ‘data.table’ and 'data.frame':  299 obs. of  5 variables:
 $ pickup_longitude : num  -74 -74 -74 -74 -74 ...
 $ pickup_latitude  : num  40.8 40.7 40.8 40.7 40.8 ...
 $ dropoff_longitude: num  -74 -74 -74 -74 -74 ...
 $ dropoff_latitude : num  40.8 40.7 40.7 40.7 40.8 ...
 $ rown             : num  1 2 3 4 5 6 7 8 9 10 ...
 - attr(*, ".internal.selfref")=<externalptr> 
drop <- lon_min[, c("pickup_longitude", "pickup_latitude", "rown")]
pick <- lon_min[, c("dropoff_longitude", "dropoff_latitude", "rown")]
colnames(drop)  <- c("lon", "lat", "rown")
colnames(pick) <- colnames(drop)
all_bin_min <- bind_rows(drop, pick)
all_bin_min$rown2 <- rep(1:nrow(all_bin_min)+1/2,each = 2)
Supplied 1196 items to be assigned to 598 items of column 'rown2' (598 unused)
leaflet(data = all_bin_min) %>% addTiles() %>%
  addCircles(~lon, ~lat) %>%
  addPolygons(data = all_bin_min, lng = ~lon, 
               lat = ~lat, 
               stroke = 0.03, color =  "blue", weight = 0.4, 
               opacity = 1.2)  %>% enableMeasurePath() 
 leaflet(data = train_joined[1:50000, ]) %>% addTiles() %>%
  addMarkers(~pickup_longitude, ~pickup_latitude, clusterOptions = markerClusterOptions()) 
train_count <- train_joined %>% 
                select(pickup_latitude, pickup_longitude) %>%
                group_by(pickup_latitude, pickup_longitude) %>%
                summarize(count = n())
train_count <- train_count[train_count$count >1,]
 leaflet(data = train_count) %>% addTiles() %>% 
 addHeatmap(lng = ~pickup_longitude, lat = ~pickup_latitude, intensity = ~count,
             blur = 20, max = 0.05, radius = 15)
train_count <- train_joined %>% 
                select(pickup_latitude, pickup_longitude, pickup_month) %>%
                group_by(pickup_latitude, pickup_longitude, pickup_month) %>%
                summarize(count = n())
train_count <- train_count[train_count$count >1,]
 leaflet(data = train_count) %>% addTiles() %>% 
 addHeatmap(lng = ~pickup_longitude, lat = ~pickup_latitude,
 layerId = ~pickup_month, group = ~pickup_month, intensity = ~count,
             blur = 20, max = 0.05, radius = 15)
count_weekday <- train_joined %>%
                  select(weekday) %>%
                  group_by(weekday) %>%
                  summarize(count = n())
count_weekday <- data.table(count_weekday)
count_weekday <- count_weekday[is.na(weekday)  ==  FALSE, ]
count_weekday <- data.frame(count_weekday)
tm <- treemap(count_weekday , index = c("weekday"),
              vSize = "count")

hctreemap(tm)
---
title: "Taxi(R) 중간발표 자료"
output:
  html_notebook: default
  html_document: default
---
## New York City Taxi Trip Duration

- Kaggle은 뉴욕시에서 택시 여행의 총 주행 거리를 예측하는 모델을 만드는 것에 도전하고 있습니다. 

- 기본 데이터 세트는 픽업 시간, 지리적 좌표, 승객 수 및 기타 여러 변수가 포함 된 NYC 택시 및 리무진위원회에서 발급 한 데이터 세트입니다.

- 우리가 2015 년에 주최 한 ECML / PKDD 여행 시간 도전과 유사하다는 것을 인정할 것입니다. 그러나 이 도전은 뒤죽박죽입니다. 

- 우리는 다른 참가자가 자신의 예측에 사용할 수있는 추가 교육 데이터를 게시하도록 (현금 상금과 함께) 당신을 격려합니다. 

- 우리는 커뮤니티에 특히 통찰력 있거나 가치있는 커널 작성자에게 보상하기 위해 격주 및 최종 상을 지정했습니다.


### 평가함수(Evalution)
- RMSLE(Root Mean Squared Logarithmic Error)
$$\epsilon = \sqrt{\frac{1}{n} \sum_{i=1}^n (\log(p_i + 1) - \log(a_i+1))^2 }$$

```
id,trip_duration
id00001,978
id00002,978
id00003,978
id00004,978
etc.
```

### Data 소개
- 경쟁 데이터 세트는 Google Cloud Platform의 Big Query에서 제공되는 2016 년 NYC Yellow Cab 여행 기록 데이터를 기반으로합니다. 

- 이 데이터는 원래 NYC 택시 및 리무진위원회 (TLC)에서 발간 한 것입니다. 

- 데이터는 이 놀이터 경쟁의 목적을 위해 샘플링되고 청소되었습니다. 
- 참가자는 개별 여행 속성에 따라 테스트 세트의 각 여행 기간을 예측해야 합니다.

### 데이터 필드

- id : 각 출장의 고유 식별자
- vendor_id : 여행 기록과 연결된 공급자를 나타내는 코드
- pickup_datetime : 미터가 작동 된 날짜와 시간
- dropoff_datetime : 미터가 분리 된 날짜와 시간
- passenger_count : 차량의 승객 수 (운전자가 입력 한 값)
- pickup_longitude : 미터가 사용 된 경도
- pickup_latitude : 미터가 사용 된 위도
- dropoff_longitude : 미터가 분리 된 경도
- dropoff_latitude : 미터가 분리 된 위도
- store_and_fwd_flag : 플래그는 자동차가 서버에 연결되어 있지 않아 여행 기록이 차량 메모리에 보관되었는지 여부를 나타냅니다. 
  - Y = 저장 및 전달; N = 상점 및 순회 여행 불가

- trip_duration : 여행 기간 (초)

* 면책 조항 : 커널에서 사용할 확장 된 변수 집합을 제공하기 위해 데이터 집합 순서에서 드롭 오프 좌표를 제거하지 않기로 결정했습니다.

### Ref 자료
- https://www.kaggle.com/wti200/exploratory-analysis-nyc-taxi-trip
- https://www.kaggle.com/ambarish/tutorialstyle-edatomodel-lb-0-391
- https://www.kaggle.com/headsortails/nyc-taxi-eda-update-the-fast-the-curious/notebook
- https://www.kaggle.com/damianpanek/interactive-eda-nytaxi-highchar-leaflet
- https://www.kaggle.com/retrospectprospect/nyc-taxi-trip-duration-eda-to-xgb
- https://www.kaggle.com/hamzaashraf/simple-to-understand-eda-v2

## 1단계. 데이터 전처리

```{r}
#install.packages(c('flexdashboard', 'TraMineR', 'leaflet', 'treemap', 'highcharter', 'zoo')

#라이브러리 로딩
library(data.table)
library(dplyr)
library(ggplot2)
library(flexdashboard)
library(TraMineR)
library(highcharter)
library(DT)
library(flexdashboard)
library(leaflet)
library(rmarkdown)
library(treemap)
library(viridisLite)
library(tidyverse)
library(geosphere)
library(caret)
library(ggmap)
library(scales)
library(ggthemes)
library(gridExtra)
library(sp)
library(lubridate)
library(grid)

rm(list=ls())
fillColor = "#ff9999"
```

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
train = read_csv("./data/train.csv")
test = read_csv("./data/test.csv")

sum(is.na(train))
sum(is.na(test))
```

```{r result='asis',  warning=FALSE}
ggplot(data=train, aes(x= trip_duration)) + 
  geom_histogram(bins = 100) +
  scale_x_log10(limits = c(NA,100000)) +
  scale_y_log10() +
  theme_bw() +
  theme(axis.title = element_text(size=16),
        axis.text = element_text(size=14)) +
  labs(x = 'Trip Duration', y = 'Count', title = 'Trip Duration') 
```

```{r}
nycData = subset(train,train$trip_duration < (60*60*24) )

ggplot(nycData,aes(x=factor(passenger_count),y=trip_duration))+geom_boxplot()+scale_y_log10()
```

```{r}
ggplot(data=nycData, aes(x= pickup_longitude)) + 
  geom_histogram(bins = 100) +
  scale_x_continuous(limits = c(-74,-73.85)) +
  theme_bw() +
  theme(axis.title = element_text(size=16),
        axis.text = element_text(size=14)) +
  labs(x = 'Longitude', y = 'Count', title = 'Longitude')
```


```{r}
ggplot(data=nycData, aes(x= pickup_latitude)) + 
  geom_histogram(bins = 100) +
  scale_x_continuous(limits = c(40.6,40.85)) +
  theme_bw() +
  theme(axis.title = element_text(size=16),
        axis.text = element_text(size=14)) +
  labs(x = 'Latitude', y = 'Count', title = 'Latitude')

```

```{r}
pick_coord <- nycData %>%
  select(pickup_longitude, pickup_latitude)
drop_coord <- nycData %>%
  select(dropoff_longitude, dropoff_latitude)

nycData$dist <- distCosine(pick_coord, drop_coord) 

nycData$haversine <- distHaversine(pick_coord, drop_coord)

nycData$bearing <- bearing(pick_coord, drop_coord) 
ggplot(data=nycData, aes(x= haversine)) + 
  geom_histogram() +
  scale_x_log10() +
  scale_y_log10() +
  theme_bw() +
  theme(axis.title = element_text(size=16),
        axis.text = element_text(size=14)) +
  labs(x = 'Distance', y = 'Count', title = 'Distance')
```

```{r}
ggplot(nycData)+
  geom_point(aes(x=haversine,y=trip_duration))+
  scale_y_log10() +
  scale_x_log10() +
  theme_bw()+
  theme(axis.title = element_text(size=16),axis.text = element_text(size=14))+
  xlab("(Distance)")+
  ylab("Duration")
```

## COMBINING TRAIN AND TEST
* combine two data sets into one to preprocess 

```{r}
train <- as.tibble(train)
test <- as.tibble(test)

combine = bind_rows(train %>% mutate(dset="train"),
                    test %>% mutate(dset="test",
                                    dropoff_datetime=NA,
                                    trip_duration=NA))

combine <- combine %>% mutate(dset = factor(dset))

train = train %>%
  mutate(pickup_datetime = ymd_hms(pickup_datetime),
         dropoff_datetime = ymd_hms(dropoff_datetime),
         vendor_id = factor(vendor_id),
         passenger_count = factor(passenger_count))
```

## INDIVISUAL FEATURE VISUALIZATION 
각 변수의 분포를 우선 살펴본다 

pickup/dropoff coordinates

* manhattan only, JFK is another notable hotspot

```{r}
set.seed(1234)
foo <- sample_n(train, 8e3)

leaflet(data = foo) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
  addCircleMarkers(~ pickup_longitude, ~pickup_latitude, radius = 1,
                   color = "blue", fillOpacity = 0.3)

leaflet(data = foo) %>% addProviderTiles("Esri.NatGeoWorldMap") %>%
  addCircleMarkers(~ dropoff_longitude, ~dropoff_latitude, radius = 1,
                   color = "blue", fillOpacity = 0.3)

```

trip_duration

* log-normal with a peak of 1000 seconds (27min)
* several short rides less than 10 sec

```{r}
train %>%
  ggplot(aes(trip_duration)) +
  geom_histogram(fill = "red", bins = 150) + 
  scale_x_log10() +
  scale_y_sqrt()
```

* homogeneous, covering between January and Jul
* drop around late January - weather 

```{r, echo=FALSE}
# Define multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols:   Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {

  # Make a list from the ... arguments and plotlist
  plots <- c(list(...), plotlist)

  numPlots = length(plots)

  # If layout is NULL, then use 'cols' to determine layout
  if (is.null(layout)) {
    # Make the panel
    # ncol: Number of columns of plots
    # nrow: Number of rows needed, calculated from # of cols
    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
                    ncol = cols, nrow = ceiling(numPlots/cols))
  }

 if (numPlots==1) {
    print(plots[[1]])

  } else {
    # Set up the page
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))

    # Make each plot, in the correct location
    for (i in 1:numPlots) {
      # Get the i,j matrix positions of the regions that contain this subplot
      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))

      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
                                      layout.pos.col = matchidx$col))
    }
  }
}
```

```{r}
# View(train)
p1 <- train %>%
  ggplot(aes(pickup_datetime)) +
  geom_histogram(fill = "red", bins = 120) +
  labs(x = "Pickup dates")

p2 <- train %>%
  ggplot(aes(dropoff_datetime)) +
  geom_histogram(fill = "blue", bins = 120) +
  labs(x = "Dropoff dates")

layout <- matrix(c(1,2),2,1,byrow=FALSE)
multiplot(p1, p2, layout=layout)
```

```{r}
p1 <- train %>%
  group_by(passenger_count) %>%
  count() %>%
  ggplot(aes(passenger_count, n, fill = passenger_count)) +
  geom_col() +
  scale_y_sqrt() +
  theme(legend.position = "none")

p2 <- train %>%
  ggplot(aes(vendor_id, fill = vendor_id)) +
  geom_bar() +
  theme(legend.position = "none")

p3 <- train %>%
  ggplot(aes(store_and_fwd_flag)) +
  geom_bar() +
  theme(legend.position = "none") +
  scale_y_log10()

p4 <- train %>%
  mutate(wday = wday(pickup_datetime, label = TRUE)) %>%
  group_by(wday, vendor_id) %>%
  count() %>%
  ggplot(aes(wday, n, colour = vendor_id)) +
  geom_point(size = 4) +
  labs(x = "Day of the week", y = "Total number of pickups") +
  theme(legend.position = "none")

p5 <- train %>%
  mutate(hpick = hour(pickup_datetime)) %>%
  group_by(hpick, vendor_id) %>%
  count() %>%
  ggplot(aes(hpick, n, color = vendor_id)) +
  geom_point(size = 4) +
  labs(x = "Hour of the day", y = "Total number of pickups") +
  theme(legend.position = "none")

layout <- matrix(c(1,2,3,4,5,5),3,2,byrow=TRUE)
multiplot(p1, p2, p3, p4, p5, layout=layout)
```

```{r}
p1 <- train %>%
  mutate(hpick = hour(pickup_datetime),
         Month = factor(month(pickup_datetime, label = TRUE))) %>%
  group_by(hpick, Month) %>%
  count() %>%
  ggplot(aes(hpick, n, color = Month)) +
  geom_line(size = 1.5) +
  labs(x = "Hour of the day", y = "count")

p2 <- train %>%
  mutate(hpick = hour(pickup_datetime),
         wday = factor(wday(pickup_datetime, label = TRUE))) %>%
  group_by(hpick, wday) %>%
  count() %>%
  ggplot(aes(hpick, n, color = wday)) +
  geom_line(size = 1.5) +
  labs(x = "Hour of the day", y = "count")

layout <- matrix(c(1,2),2,1,byrow=FALSE)
multiplot(p1, p2, layout=layout)
```

* 1 월과 6 월은 여행 횟수가 적지만 3 월과 4 월은 바쁜 달입니다.
* 주말 (토요일과 일요일, 플러스에서 연장까지) 이른 아침에는 더 높은 여행 숫자를 갖지만 오전에는 5시에서 10 시까 지 낮습니다

## FEATURE RELATIONS 
Pickup date/time vs trip_duration
```{r}
p1 <- train %>%
  mutate(wday = wday(pickup_datetime, label = TRUE)) %>%
  group_by(wday, vendor_id) %>%
  summarise(median_duration = median(trip_duration)/60) %>%
  ggplot(aes(wday, median_duration, color = vendor_id)) +
  geom_point(size = 4) +
  labs(x = "Day of the week", y = "Median trip duration [min]")

p2 <- train %>%
  mutate(hpick = hour(pickup_datetime)) %>%
  group_by(hpick, vendor_id) %>%
  summarise(median_duration = median(trip_duration)/60) %>%
  ggplot(aes(hpick, median_duration, color = vendor_id)) +
  geom_smooth(method = "loess", span = 1/2) +
  geom_point(size = 4) +
  labs(x = "Hour of the day", y = "Median trip duration [min]") +
  theme(legend.position = "none")

layout <- matrix(c(1,2),2,1,byrow=FALSE)
multiplot(p1, p2, layout=layout)
```


* 더 자주 여행하는 벤더 2는 지속적으로 더 높은 여행 기간을 가집니다.
* 이른 오후의 최고점과 5-6am와 8pm 주위에 dips.

## Passenger count and Vendor vs trip_duration

```{r}
train %>%
  ggplot(aes(trip_duration, fill = vendor_id)) +
  geom_density(position = "stack") +
  scale_x_log10()

train %>%
  group_by(vendor_id) %>%
  summarise(mean_duration = mean(trip_duration),
            median_duration = median(trip_duration))
```

* 중앙값은 매우 유사하지만, 평균값이 긴 기간의 대부분을 포함하는 벤더 2에 의해 평균값이 비뚤어 질 가능성이 있습니다

## Feature Engineering
- 픽업 및 드롭 오프 위치 사이의 거리 (킬로미터)
- 픽업 포인트와 드롭 오프 포인트의 좌표로부터 두 점 사이의 거리를 계산할 수 있습니다. 

- 이 거리를 계산하기 위해 우리는 지구권 패키지의 distHaversine 함수를 사용하고 있습니다. 이 방법은 구형 지구의 두 점 사이의 최단 거리를 제공합니다.

```{r}
train = as.data.table(train)

train <- train[,distance_km := 
                     distHaversine(matrix(c(pickup_longitude, pickup_latitude), ncol = 2),
                     matrix(c(dropoff_longitude,dropoff_latitude), ncol = 2))/1000
              ]
train %>% 
  ggplot(aes(x=distance_km)) + 
  geom_histogram(bins=4000, fill="red")+
  theme_bw()+theme(axis.title = element_text(size=11),axis.text = element_text(size=8))+
  ylab("Density")+coord_cartesian(x=c(0,25))
```

- 테스트 속도는 테스트 데이터 세트에서 사용할 수 없습니다. 그러나 그것은 어떤 종류의 패턴이 트래픽에 있는지를 보는 것을 도울 수 있습니다.

```{r}
train[,speed:=(distance_km)/(trip_duration/3600)]

train %>% 
  ggplot(aes(x=speed)) + 
  geom_histogram(bins=4000, fill="red")+
  theme_bw()+theme(axis.title = element_text(size=11),axis.text = element_text(size=8))+
  ylab("Density")+coord_cartesian(x=c(0,50))
```

```{r}
summary(train$speed)
```

- 9285km / h 속도의 특정 rides가 있습니다. 우리는 이상 치 분석 (Outlier Analysis) 부분에서 이를 조사 할 것입니다. 그러나 평균 속도는 15km / h입니다. 

```{r, echo=TRUE}
train$pickup_hour <- hour(train$pickup_datetime)
train$pickup_week <- week(train$pickup_datetime)
train$pickup_month <- month(train$pickup_datetime)
train$pickup_weekdays <- weekdays(train$pickup_datetime)
train$pickup_weekend <- ifelse(train$pickup_weekdays==1 | train$pickup_weekdays==7,"Weekend","not-Weekend")

train[,pickup_datetime:=as.Date(pickup_datetime)]
train[,dropoff_datetime:=as.Date(dropoff_datetime)]

train[,":="(
   pickup_yday=yday(pickup_datetime)
  ,pickup_mday=mday(pickup_datetime)
)]

train %>% 
  group_by(pickup_hour) %>% 
  summarize(mean_speed = mean(speed),n()) %>% 
  ggplot(aes(x=pickup_hour,y=mean_speed))+
  geom_smooth(method = 'loess',color="grey10")+
  geom_point(color="red")+coord_cartesian(ylim=c(10,25))+theme_bw()
```

- 하루 평균 속도가 상당히 낮습니다. 분명히 트래픽이 많기 때문입니다.

## Feature Visualisation

```{r}
library(corrplot)
corr_features = train[,.(pickup_hour, pickup_week, pickup_month,pickup_yday, pickup_mday,passenger_count,trip_duration,distance_km)]

corrplot(cor(corr_features, use='complete.obs'), type='lower')
```

- 변수의 어느 것도 trip_duration과 상관 관계가 없기 때문에 이것은 매우 불안정한 구성입니다. 

- 거리가 상관 관계가있는 유일한 거리이지만 테스트 세트에는 해당 기능이 없습니다. 기능의 일부가 대상 변수와 상호 연관 될 수도 있지만 조사해야합니다. 

- 피쳐와 타겟 변수간에 상관 관계가 없다는 것은 트립 시간을 예측하기 위해 외부 피쳐를 찾아야한다는 것을 의미합니다.

```{r}
plot1 <-train[, list(mean_trip_duration = mean(trip_duration)), by = pickup_weekdays] %>%
  ggplot(aes(x = pickup_weekdays, y = mean_trip_duration)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Month', y = 'Mean Trip Duration', title = 'Mean Trip duration by weekdays')

grid.arrange(plot1)
```

```{r}
plot1 <-train[, list(mean_trip_duration = mean(trip_duration)), by = pickup_hour] %>%
  ggplot(aes(x = as.factor(pickup_hour), y = mean_trip_duration)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Hours', y = 'Mean Trip Duration', title = 'Mean Trip duration by hour of the day')

plot2 = train[,.N, by=pickup_hour] %>%
  ggplot(aes(x=pickup_hour, y=N)) + 
  geom_bar(stat='identity', fill='steelblue') + 
  labs(x='', y='Number of Rides', title='Total Rides Per Hour')

grid.arrange(plot1, plot2, ncol =2)
```

## External Data
- Fastest Routes
[oscarleo](https://www.kaggle.com/oscarleo) Open source Routing Machine([OSRM](http://project-osrm.org/))

- Open Source Routing Machine, OSRM을 사용하여 oscarleo가 유용한 데이터 세트를 제공합니다. 

- 픽업에서 드롭 오프 위치까지의 가장 빠른 경로와 해당 시간.
가장 빠른 노선을위한 거리의 수.

- 예를 들어 고속도로 진입과 같은 여행 당 기동 수.
- 예를 들어 좌회전이나 우회전과 같은 여행 당 길 찾기.

```{r}
suppressMessages({
fastest_route_train = read_csv("./data/new-york-city-taxi-with-osrm/fastest_routes_train_part_1.csv")
})
dtrain = merge(train, fastest_route_train, by="id")

dtrain[,number_of_streets := number_of_steps - 1]
plot1 <- 
  dtrain[, list(mean_trip_duration = mean(total_travel_time)), by = number_of_streets] %>%
  ggplot(aes(x = as.factor(number_of_streets), y = mean_trip_duration)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Number of Streets', y = 'Mean Trip Duration', title = 'Mean Trip duration by Number of Streets')

plot2 <- dtrain[, list(Number_of_Rides = .N), by = number_of_streets] %>%
  ggplot(aes(x = as.factor(number_of_streets), y = Number_of_Rides)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Number of Streets', y = 'Number of Trips', title = 'Number of Rides by Number of Streets')

plot3 <- dtrain[, list(mean_distance = mean(total_distance)/1000), by = number_of_streets] %>%
  ggplot(aes(x = as.factor(number_of_streets), y = mean_distance)) +
  geom_bar(stat = 'identity', fill = 'steelblue') +
  labs(x = 'Number of Streets', y = 'Mean Trip Distnace(km)', title = 'Mean Trip Distance by Number of Streets')

grid.arrange(plot1,plot2, plot3)
```

- 몇 가지 더 확인하고 싶습니다. 예를 들어 실제 여행 시간과 가장 빠른 경로 사이의 시간 차이가 얼마나 큰지를 예로 들 수 있습니다.

```{r}
plot1 <- dtrain %>%
  ggplot(aes(trip_duration)) +
  geom_density(fill = "red", alpha = 0.5) +
  geom_density(aes(total_travel_time), fill = "blue", alpha = 0.5) +
  scale_x_log10() +
  coord_cartesian(xlim = c(5e1, 8e3))

dtrain[,diff:= abs(trip_duration-total_travel_time)]

dtrain[,number_of_steps:= ifelse(number_of_steps>= 25, 25, number_of_steps)]

plot2 =  dtrain[, list(mean_distance_km = mean(diff)), by=number_of_steps-1] %>%
  ggplot(aes(x=number_of_steps, y=mean_distance_km)) + 
  geom_bar(stat='identity', fill='steelblue') + 
  labs(x='Number of Streets', y='|TripDuration - TotalTravelTime)|')

grid.arrange(plot1, plot2, ncol=2)
```


- 파란색 그림은 가장 빠른 경로 밀도 그림입니다. 실제 여행 시간과 비슷하지만 교대로 보입니다.

- 두 번째 플롯에서 우리는 실제 이동량과 가장 빠른 경로 시간 간의 절대적인 차이가 거리의 수가 증가함에 따라 증가한다는 것을 관찰합니다. 

- 오늘날의 가장 빠른 경로가 과거의 실제 경로와 다른 확률이 단계 증가의 수로 증가하기 때문에 논리적입니다.

- 가장 빠른 경로 데이터를 사용하여 실제 경로에 대해 의미있는 것을 말하는 방법을 이해하는 것은 정말 어렵습니다. 오늘 가장 빠른 길은 내일 또는 과거의 가장 빠른 길이 아니기 때문에. 

- 따라서 훈련 세트의 경로는 OSRM에서 제안한 경로와 다릅니다. 내가 가장 다른 점은 가장 빠른 경로와 관련된 기능이 훈련 세트의 rides에 대해별로 말하지 않는다는 것입니다.

## Google Distance Matrix

- 이 데이터 세트는 Debanjan이 Google Maps API를 사용하여 제공합니다. 

- 적어도 이 대회의 모든 라이딩에 사용할 수있는 데이터가 있다면 많은 것을 약속합니다. 

- 데이터는 훈련 세트의 하위 집합에 대해서만 제공되므로 나머지 데이터는 잠깐 기다리고 있습니다. 이 데이터 세트의 중요한 정보는 Google 기간이라는 두 위치 간의 과거 평균 지속 시간입니다. 

- 나는 Google 기간과 실제 기간의 차이로 새로운 기능을 만들어 낼 것입니다. 차이가 '지연'보다 '0'보다 작으면 '초기 도착'이 있습니다.

```{r}
suppressMessages({
google_dist = read_csv("./data/new-york-city-taxi-with-osrm/train_distance_matrix.csv")
})

google_dist = data.table(google_dist)
google_dist[, diff := google_duration-trip_duration]

plot1 <- google_dist %>%
  ggplot(aes(trip_duration)) +
  geom_density(fill = "red", alpha = 0.5) +
  geom_density(aes(google_duration), fill = "blue", alpha = 0.5) +
  scale_x_log10() +
  coord_cartesian(xlim = c(5e1, 8e3))


plot2 = google_dist %>% 
  ggplot(aes(x=diff)) + 
  geom_histogram(bins=20000, fill="red")+
  theme_bw()+theme(axis.title = element_text(size=12),axis.text = element_text(size=12))+
  ylab("Density")+coord_cartesian(x=c(-2000,2000))

grid.arrange(plot1, plot2, ncol=2)
```

- 파란색 줄거리는 Google 기간이며 빨간색은 실제 지속 시간입니다. 

- Google 기간이 더 비뚤어졌습니다. 실제 지속 시간은 더 높은 분산 (더 부풀어 오른)과 더 두꺼운 꼬리를가집니다.

- 두 번째 플롯은 Google 기간과 실제 기간 간의 시간 차이에 대한 막대 그래프입니다. 

- 왼쪽보다 0의 오른쪽에 더 많은 관측이 있다는 것을 관찰 할 수 있습니다. 즉, '지연'보다 '조기 도착'이 더 많은 'rides'가 있습니다. 

## Weather Data

```{r}
weather = fread("./data/new-york-city-taxi-with-osrm/weather_data_nyc_centralpark_2016.csv")
weather <- weather %>%
  mutate(date = dmy(date),
         rain = as.numeric(ifelse(precipitation == "T", "0.01", precipitation)),
         s_fall = as.numeric(ifelse(`snow fall` == "T", "0.01", `snow fall`)),
         s_depth = as.numeric(ifelse(`snow depth` == "T", "0.01", `snow depth`)),
         all_precip = s_fall + rain,
         has_snow = (s_fall > 0) | (s_depth > 0),
         has_rain = rain > 0,
         max_temp = `maximum temerature`,
         min_temp = `minimum temperature`)


weather = as.data.table(weather)
weather[, c("precipitation", "snow fall", "snow depth", "maximum temerature", "minimum temperature") := NULL]

setkey(dtrain, dropoff_datetime)
setkey(weather, date)

dtrain = weather[dtrain]
```

- 날씨가 두 가지 방법으로 여행 시간에 영향을 미친다고 생각할 것입니다.

- 눈 : 특정 양의 눈이 내린 후에 만 역할을해야합니다.
- 비 : 눈이 일정량의 강수량 후에 만 역할을해야합니다.

```{r}
plot1 = dtrain %>%
  group_by(pickup_hour, has_snow) %>%
  summarise(duration = mean(trip_duration)) %>%
  ggplot(aes(pickup_hour,duration, color = has_snow)) +
  geom_jitter(width = 0.01, size = 2) +
  labs(x = "hour", y = "trip duration")

plot2 = dtrain %>% 
  group_by(pickup_hour, has_snow) %>%
  summarise(distance = mean(distance_km)) %>%
  ggplot(aes(pickup_hour,distance, color = has_snow)) +
  geom_jitter(width = 0.01, size = 2) +
  labs(x = "hour", y = "Distance Covered")

grid.arrange(plot1, plot2,ncol=1)
```

- 첫 번째 줄거리에서 눈이 내릴 때 아침 10시에서 저녁 20시 사이에 눈이 내리지 않는 날에 비해 평균 여행 소요 시간이 짧다는 결론을 얻을 수 있습니다. 

- 일반적으로 걷거나 자전거를 타는 사람들에게 눈이 내릴 때 목적지에 도달하기 위해 택시를 타기 때문에 아주 이상하지 않습니다.

```{r}
plot1 = dtrain %>%
  group_by(pickup_hour, has_rain) %>%
  summarise(duration = mean(trip_duration)) %>%
  ggplot(aes(pickup_hour,duration, color = has_rain)) +
  geom_jitter(width = 0.01, size = 2) +
  labs(x = "hour", y = "trip duration")

plot2 = dtrain %>% 
  group_by(pickup_hour, has_rain) %>%
  summarise(distance = mean(distance_km)) %>%
  ggplot(aes(pickup_hour,distance, color = has_rain)) +
  geom_jitter(width = 0.01, size = 2) +
  labs(x = "hour", y = "Distance Covered")

grid.arrange(plot1, plot2,ncol=1)
```

- 비가 내리는 날과 평균 여행 기간이 아닌 날 사이에는 별 차이가 없습니다.

- 그러나 나는 비오는 날에 덮힌 거리가 비오는 날과 비교하여 적다는 것을 알 수 있습니다. 일반적으로 짧은 거리를 걷거나 자전거를 타는 사람들이 목적지로 택시를 타는 것을 선호하기 때문에 이것은 논리적입니다.

- 2016 년 NYC의 날씨와 함께 데이터 세트를 사용하기로 결정 했으므로 이 데이터 세트를 합치려면 몇 가지 데이터가 필요합니다.

## [METAR](https://en.wikipedia.org/wiki/METAR)

- METAR는 날씨 정보를 보고 하는 형식입니다. 
- METAR 기상 예보는 비행 전 날씨 브리핑의 일부를 수행하는 조종사와 기상 예측에 도움이되는 집계 된 METAR 정보를 사용하는 기상 학자에 의해 주로 사용됩니다.

```{r}
train <-  fread("./data/train.csv")

weather_nyc <-  fread("./data/KNYC_Metars.csv")

train[, pi_dt_shift :=  paste(substr(pickup_datetime, 1, 13), ":00:00", sep = "")]
train[, df_dt_shift := paste(substr(dropoff_datetime, 1, 13), ":00:00", sep = "")]

train_joined <- dplyr::left_join(train, weather_nyc, by  = c("pi_dt_shift" = "Time"))

train_joined$Conditions[is.na(train_joined$Conditions) ==  TRUE] <- "Unknown"

weather_condition_freq <- train_joined %>%
  group_by(Conditions) %>%
  select(Conditions,trip_duration ) %>%
  summarize(count = n(), 
            mean_dur = mean(trip_duration, na.rm = TRUE), 
            sd_dur =   sd(trip_duration, na.rm = TRUE), 
            median_dur = median(trip_duration, na.rm = TRUE))


datatable(weather_condition_freq)
```

- 아래의 그림은 사용자가 픽업 택시를 다른 기상 조건에 얼마나 자주 의존하는지 보여줍니다.

- NA가 있는 조건 값을 '알수 없는 카테고리'로 변경하기로 결정했습니다.

- 가장 빈번한 그룹은 'Clear' 조건을 가진 그룹이라는 것이 분명합니다.

```{r}
highchart()%>%
  hc_add_series(weather_condition_freq, "spline", hcaes(x =  Conditions, y = mean_dur), name = "Mean Trip Duration") %>%
  hc_add_series(weather_condition_freq, "spline", hcaes(x =  Conditions, y = median_dur), name = "Median Trip Duration") %>%
  hc_add_series(weather_condition_freq, "spline", hcaes(x =  Conditions, y = sd_dur), name = "SD Trip Duration") %>%
  hc_plotOptions(series = list(
    showInLegend = TRUE, 
    pointFormat = "{point.y}%"
  ), 
  column = list(colorByPoint = TRUE)) %>%
  hc_subtitle(text = "Count by Conditions Caegories") %>%
  hc_credits(
    enabled = TRUE, 
    text = "Source: Kaggle", 
    href = "https://kaggle.com/damianpanek", 
    style = list(fontSize = "12px")
  ) %>%
  hc_add_theme(hc_theme_google())
```

- 작은 데이터 변환. 일 / 월 및 관찰 요일에 대한 정보를 얻고 싶습니다.

```{r}
train_joined <- data.table(train_joined)
train_joined <- train_joined[is.na(pickup_datetime) == FALSE,  ]

train_joined[, pickup_datetime := as.POSIXct(pickup_datetime, format = "%Y-%m-%d %H:%M:%S")]
train_joined[, dropoff_datetime := as.POSIXct(dropoff_datetime, format = "%Y-%m-%d %H:%M:%S")]
train_joined[, pickup_day := format(pickup_datetime, "%Y-%m-%d")]
train_joined[, pickup_month := format(pickup_datetime, "%Y-%m")]

train_joined[, dropoff_day := format(dropoff_datetime, "%Y-%m-%d")]
train_joined[, dropoff_month := format(dropoff_datetime, "%Y-%m")]

train_joined[, weekday := weekdays(pickup_datetime)]
```

- Summary Statistics for Tempertarure in NYC taxi dataset

```{r}
weather_temp_day <-  train_joined %>% 
  group_by(pickup_day) %>%
  select(pickup_day, Temp., Conditions) %>%
  summarize(count = n(), 
            min = min(Temp., na.rm = TRUE), 
            max = max(Temp., na.rm = TRUE), 
            sd_dur = sd(Temp., na.rm = TRUE))

hchart(weather_temp_day, 
        type = "columnrange", 
        hcaes(x = pickup_day, low = min, high = max, color = sd_dur)) %>%
        hc_chart(polar = TRUE) %>%
    hc_yAxis(max = 30,  min = -10, labels = list(format = "{value} "), 
             showFirstLabel = FALSE) %>%
  hc_xAxis(
  title = list(text = ""), gridLineWidth = 0.5,
  labels = list(format = "{value: %b}")) %>%
  hc_add_theme(hc_theme_google()) %>%
hc_title(text = "Min/Max temperature daily, coloured by SD(Temp)")
```

## Leaflet section
- 먼저 순서를 만들려면 행을 끌어서 선택해야 합니다. 다음 makecluster 옵션을 사용하여 전단을 작성하기로 결정했습니다.

```{r}
#install.packages('leaflet.extras')
library(leaflet)
library(leaflet.extras)

lon_lat <- train_joined[, c("pickup_longitude", "pickup_latitude", 
"dropoff_longitude", "dropoff_latitude")]

lon_lat$rown <- as.numeric(rownames(lon_lat))

lon_min <- lon_lat[rown < 300 ,]
str(lon_min)
drop <- lon_min[, c("pickup_longitude", "pickup_latitude", "rown")]
pick <- lon_min[, c("dropoff_longitude", "dropoff_latitude", "rown")]

colnames(drop)  <- c("lon", "lat", "rown")
colnames(pick) <- colnames(drop)

all_bin_min <- bind_rows(drop, pick)
all_bin_min$rown2 <- rep(1:nrow(all_bin_min)+1/2,each = 2)


leaflet(data = all_bin_min) %>% addTiles() %>%
  addCircles(~lon, ~lat) %>%
  addPolygons(data = all_bin_min, lng = ~lon, 
               lat = ~lat, 
               stroke = 0.03, color =  "blue", weight = 0.4, 
               opacity = 1.2)  %>% enableMeasurePath() 
```

- Leaflex plot with makecluster options 

```{r}
 leaflet(data = train_joined[1:50000, ]) %>% addTiles() %>%
  addMarkers(~pickup_longitude, ~pickup_latitude, clusterOptions = markerClusterOptions()) 
```

- Leaflet heatmap 

```{r}
train_count <- train_joined %>% 
                select(pickup_latitude, pickup_longitude) %>%
                group_by(pickup_latitude, pickup_longitude) %>%
                summarize(count = n())


train_count <- train_count[train_count$count >1,]



 leaflet(data = train_count) %>% addTiles() %>% 
 addHeatmap(lng = ~pickup_longitude, lat = ~pickup_latitude, intensity = ~count,
             blur = 20, max = 0.05, radius = 15)
```

- Pickup grouped by month

```{r}
train_count <- train_joined %>% 
                select(pickup_latitude, pickup_longitude, pickup_month) %>%
                group_by(pickup_latitude, pickup_longitude, pickup_month) %>%
                summarize(count = n())

train_count <- train_count[train_count$count >1,]


 leaflet(data = train_count) %>% addTiles() %>% 
 addHeatmap(lng = ~pickup_longitude, lat = ~pickup_latitude,
 layerId = ~pickup_month, group = ~pickup_month, intensity = ~count,
             blur = 20, max = 0.05, radius = 15)
```

- Frequency by  day of week :)

```{r}
count_weekday <- train_joined %>%
                  select(weekday) %>%
                  group_by(weekday) %>%
                  summarize(count = n())

count_weekday <- data.table(count_weekday)


count_weekday <- count_weekday[is.na(weekday)  ==  FALSE, ]

count_weekday <- data.frame(count_weekday)

tm <- treemap(count_weekday , index = c("weekday"),
              vSize = "count")

hctreemap(tm)
```

